Network Device Identifies Packet Drop Sources via Error Rate Analysis
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Solution Overview
Problem
Current deterministic networking and Low Power and Lossy Networks (LLNs) face challenges in identifying and addressing packet drops caused by software defects, as existing routing protocols rely on dynamically computed link metrics that do not account for misbehaving nodes due to software issues.
Innovation Solution
A network device compares observed and expected packet error rates to identify a particular node as a source of packet drops by analyzing intersecting paths and initiating packet trains to pinpoint the issue, using machine learning to correlate network performance metrics and take corrective measures such as rerouting traffic to avoid the misbehaving node.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamically computed link metrics (e.g., ETX values) are used to account for link quality degradation, then link quality can be adapted to changing environmental conditions, but packet drops caused by software defects at local devices cannot be detected or addressed
Solution Approach 1:
The patent segments the packet error rate analysis by dividing the network path into individual links and further segmenting by candidate misbehaving nodes. Each link's observed packet error rate is compared against expected rates, and nodes are individually evaluated as potential sources of packet drops by analyzing their specific contribution to errors on paths they traverse.
Solution Approach 2:
The patent implements feedback mechanisms where observed packet error rates are continuously monitored and compared against expected rates. When discrepancies are detected, the system feeds back this information to identify candidate misbehaving nodes and adjusts routing decisions accordingly, creating a closed-loop system that adapts to software defects.
2Measurement precision
If packet trains are initiated to pinpoint the source of packet drops, then measurement precision of node behavior can be improved, but network overhead and complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-calculating expected packet error rates for each link and path before actual packet transmission. These expected rates are stored and used as baseline references when initiating packet trains, allowing for rapid comparison and identification of anomalies without requiring complex real-time calculations during packet drop detection.
Solution Approach 2:
The patent applies partial action by initiating packet trains only on paths where packet drops are suspected, rather than continuously monitoring all network paths. This selective approach reduces network overhead while maintaining sufficient measurement precision for identifying misbehaving nodes.
3Reliability
If routing protocols avoid misbehaving nodes identified by software defects, then network reliability improves, but path costs may increase due to suboptimal routing
Solution Approach 1:
The patent implements dynamic routing where the network continuously adapts path selection based on real-time identification of misbehaving nodes. Routing paths are not static but dynamically adjusted to avoid nodes exhibiting software defects while maintaining efficiency by selecting from available optimal paths that do not include problematic nodes.
Solution Approach 2:
The patent changes routing parameters by modifying path selection criteria to include reliability metrics alongside traditional cost metrics. When misbehaving nodes are identified, the routing protocol adjusts its parameters to prioritize paths with higher reliability even if they have slightly higher traditional costs, achieving a balance between reliability and efficiency.
Data Source
AI summary
In one embodiment, a device in a network performs a first comparison between observed and expected packet error rates for a first path in the network. The device identifies one or more intersecting paths in the network that intersect the first path. The device performs one or more additional comparisons between observed and expected packet error rates for the intersecting paths that intersect the first path. The device identifies a particular node along the first path as a source of packet drops based on the first comparison between the observed and expected packet error rates for the first path and on the one or more additional comparisons between the observed and expected packet error rates for the intersecting paths that intersect the first path.